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This afternoon I posted some tips on how to present a new* programming language to HN: https://news.ycombinator.com/item?id=46608577. It occurred to me that HN has a tradition of posts called "The {name} programming language" (part of the long tradition of papers and books with such titles) and it might be fun to track them down. I tried to keep only the interesting ones:

https://news.ycombinator.com/thelang

Similarly, Show HNs of programming languages are at https://news.ycombinator.com/showlang.

These are curated lists so they're frozen in time. Maybe we can figure out how to update them.

A few famous cases:

The Go Programming Language - https://news.ycombinator.com/item?id=934142 - Nov 2009 (219 comments)

The Rust programming language - https://news.ycombinator.com/item?id=1498528 - July 2010 (44 comments)

The Julia Programming Language - https://news.ycombinator.com/item?id=3606380 - Feb 2012 (203 comments)

The Swift Programming Language - https://news.ycombinator.com/item?id=7835099 - June 2014 (926 comments)

But the obscure and esoteric ones are the most fun.

(* where 'new' might mean old, of course - https://news.ycombinator.com/item?id=23459210)

I am curious how people are doing RAG locally with minimal dependencies for internal code or complex documents?

Are you using a vector database, some type of semantic search, a knowledge graph, a hypergraph?

It has been 120 hours (5 days) since the internet shutdown in Iran began. While international phone calls have started working again, data remains blocked.

I am looking for technical solutions to establish resilient, long-term communication channels that can bypass such shutdowns. What are the most viable options for peer-to-peer messaging, mesh networks, or satellite-based solutions that don't rely on local ISP infrastructure?

(This is not a problem-solving rant this is a I need to release my thoughts cuz no one in my life understands rant)

Not sure where else to turn to but I'm extremely embarrassed to say we're nearing the 1 year anniversary of my Feb graduation (*course 6*) and I'm still unemployed, to the dismay of me and my family. I've applied to hundreds of jobs, tailored my resume with tech folks who regularly hire, tailored cover letters, gotten referrals, spoken to relevant connections in my network, done really well in interviews, all to no avail. The feedback I've received from asking employers who rejected me is never something wrong about me, just that they found someone else with pre-existing experience in that particular industry or tech stack. How am I supposed to compete with that at an entry level? And the longer I go without work, the worse it gets in the eyes of employers. I have two internships from back in undergrad as my "work experience" but that's it, one at a known company and one at a startup. My personal projects were not super intensive unfortunately, but I'm not sure how much that's affecting me at this point. Given the way things are going in the world, I remove certain tech sectors from consideration, but I really don't think that should be a handicap.

I knew the job market was bad going into it, but recently, I've genuinely fallen into depression. It feels like I was sold this lie that the MIT name would open doors previously inaccessible to me, but nothing seems to be helping me land a job. Sucks more when I run into old friends who can't even hide their shock that I'm still unemployed. So I have to pretend this is just a gap year and all part of the plan. I'm starting to come to terms with the fact that I might never work in industry as a *software engineer or in tech*, and that sucks! Maybe it's already time for a career change, I don't know to what. I never felt too good about myself at MIT compared to others and so this all feels like proof that I'm not skilled enough to work in my chosen field.

I can't even do my hobbies with all this free time because I spend a lot of it applying to jobs, doomscrolling, and sulking. I am really grateful that I was able to move back home with my parents. I think they were happy to have me back for a bit. But now I'm starting to feel like a drag and burden, especially as the *middle child sister* who’s just… there. I feel like a firework that exploded in bursts of color (everyone ooed and ah-ed), and then... nothing. I'm considering starting some volunteer/side projects, but persistently, in the back of my mind, is this voice telling me I'm worthless because I can't make any money. I am a failure.

I have ADHD. I think. Pretty sure. I have thoughts, ideas, projects, concepts, links, things to read... fired at my brain all day every day. I can go deep on a topic for hours, but then be hit by a barrage of micro ideas. I really struggle to stay on track and focus. Oh and I run a business, manage people, try to make a profit. It's hard. And kids. And life?

I think there is a founder/ADHD thing. Paul Graham thinks so. Maybe even a tech person angle. What have other people experienced?

And how do others cope? I don't really know this world. I do know that my old boss once called me a "flagitating laser beam". I think he meant distracted. I use a bunch of systems to cope. For a long time lists, and then Asana. Asana ruled my life. I just built my own thing to capture tasks, projects, but also knowlegde. Not sure if it will help we will see.

So tell me:

- Who else feels this way? - How do you manage? - Oh and how do you switch off? That is hard

What are some good resources viz. books/papers/articles/videos/etc. to study about the three domains listed above (from Basics to Advanced)?

1) Quantum Computation: What exactly are the abstract models of computation here? Are the Classical Computation models i.e. https://en.wikipedia.org/wiki/Model_of_computation applicable? What other new models have been invented?

2) Quantum Computers: What is the Physics, Organization and Architecture of these? In classical computers you have semiconductor physics, electronic elements and voltage thresholds mapping to logical 1's and 0's. This is then used to build layers of abstractions. What are their equivalents in a quantum computer? https://en.wikipedia.org/wiki/Quantum_computing has a lot of info. but not quite structured for understanding.

3) Quantum Programming: A lot is mentioned at https://en.wikipedia.org/wiki/Quantum_programming and Amazon lists a bunch of books on this topic but am not quite clear on how everything fits. Also as i understand, quantum computing/programming can be simulated on classical hardware but am not clear on the how.

PS: Some detailed examples as to how quantum computers/programming actually help you solve problems which cannot be solved on classical computers would be helpful to bring everything together. Shor's algorithm (https://en.wikipedia.org/wiki/Shor%27s_algorithm) is often mentioned but perhaps starting with a far simpler example would be more accessible.

PPS: In particular; I would love to hear from folks who actually study/research/work in this domain regarding what they actually do, its real-world applicabilities and how to go about learning the subject.

When traversing a public network. Let’s agree going recursive (WireGuard inside VXLAN inside WireGuard) is a bad idea.

What are you working on? Any new ideas that you're thinking about?

I’m trying to understand a discrepancy between Lichess’s analysis board and my own Stockfish setup.

On Lichess (browser-based analysis), Stockfish reports close to 1 MN/s on my Redmi Note 14 Pro. However, when I run Stockfish locally via a Python program using the native executable, I only see around 600 kN/s.

What’s confusing is that despite the higher reported speed, Lichess takes about 2:30 to reach depth 30, while my local setup reaches depth 30 in about 53 seconds, even though it reports a lower N/s. Lichess also appears much more “active” in terms of frequent evaluation updates.

I suspect this has to do with how N/s is measured or displayed (instantaneous vs average), differences in search configuration (continuous search vs restarts, MultiPV, hash reuse), or overhead from the way the engine is driven (e.g., UI or I/O throttling). It also raises the question of whether “depth 30” is directly comparable across different frontends.

Has anyone looked into how Lichess reports Stockfish speed, or why a setup showing higher N/s can still take significantly longer to reach the same nominal depth?

Yesterday my production app went down. The cause? DigitalOcean's managed PostgreSQL update broke private VPC connectivity to their managed Kubernetes.

Public endpoint worked. Private endpoint timed out. Root cause: a Cilium bug (#34503) where ARP entries go stale after infrastructure changes.

DO support responded relatively quickly (<12hrs). Their fix? Deploy a DaemonSet from a random GitHub user to ping stale ARP entries every 10 seconds. The upstream Cilium fix is merged but not yet deployed to DOKS. No ETA.

I chose managed services specifically to avoid ops emergencies. We're a tiny startup paying the premium so someone else handles this. Instead, I spent late night hours debugging VPC routing issues in a networking layer I don't control.

HN's usual advice is "just use managed services, focus on the business." Generally good advice. But managed doesn't mean worry-free, it means trading your failure modes for the vendor's failure modes. You're not choosing between problems and no problems. You're choosing between problems you control and (fewer?) problems you don't.

Still using DO. Still using managed services. Just with fewer illusions about what "managed" means.

I’m building an open-source meeting-detection engine (Node + Rust via napi-rs) that uses OS-level signals to detect when meetings start/end.

macOS support is solid, but I’m missing Windows coverage because I don’t have a Windows machine.

I’m looking for contributors to help implement/test:

• process detection (Zoom / Teams / Webex)

• active window/title enumeration

• basic network signals

Repo + docs: https://www.npmjs.com/package/meeting-detection

Happy to break this into small, well-scoped issues.

And I am loving it. After a couple of months on Liquid Glass it is very refreshing to be back with somehow sane window shapes and icons in Sequoia. Still hard to believe Apple shipped Tahoe in its current state. If things don't improve with macOS 27 this will be my last Mac. The downgrade was a hassle but nothing too bad.

I used every MacOS version since OS 9.1.

I followed this guide: https://www.youtube.com/watch?v=uQ4RZHehp0k

Intel has spent the last decade chasing moonshots: AI accelerators, Optane, RealSense, Thunderbolt branding. All while slowly bleeding out cultural relevance. Meanwhile, gamers, devs and power users are quietly revolting against walled gardens and bloated software. Windows is being shoehorned into an "agentic OS," consoles are doubling down on subscription models, and users are mourning the loss of simplicity and control.

When was the last time you were unpleasantly surprised by a big, required update when you try to play a game?

Intel has a once-in-a-decade chance to blow the doors off the console wars and make Linux mainstream. But it requires something Intel hasn't shown in years: restraint and aggression.

Here's the play:

1. Build the box: Take Panther Lake. Stick it in a boring little box that fits under the TV. No RGB. No gimmicks. Just raw performance in a console-size form factor.

2. Ship SteamOS: Boot it straight into Big Picture mode. No custom UI. No Intel launcher. No "ecosystem." The heavy lifting is already done: Steam, Proton, Vulkan. Valve did the hard work already, Intel just needs to leverage it.

3. Sell at cost: Price it like a weapon. Undercut consoles. Forget margins; this is about relevance. Get it on the shelf at Best Buy. Intel Arc becomes a household name overnight. Gamers stop asking "Can it run Crysis?" and start asking "Does it run on Arc?"

4. Walk away: No subscriptions. No proprietary APIs. No marketing fluff or Intel trying to be relevant with cheeky references to the past. Don't try to own the platform, and DON'T ALLOW FEATURE CREEP!! Just set the grenade, pull the pin, and let the market do the rest.

This play works expertly, because gamers are fed up, Valve already proved Linux gaming works, the open ecosystem will catch the giants Sony and Microsoft sleeping. Nintendo will just keep doing what it's always done. Intel Arc gains legitimacy in the one place where driver optimization matters more than CUDA.

IF Intel does this, here's what happens:

1. Sony and Microsoft scramble to defend their turf. Expect rushed subscription perks, price cuts, and more "exclusive content." However, the damage is done. The idea of an open, console-like PC (crucially, WITHOUT PC branding) becomes mainstream.

2. Valve wins big. SteamOS becomes the de facto standard living room OS. Proton development accelerates. Linux gaming stops being a niche and becomes a cultural norm.

3. NVIDIA feels the heat. Intel Arc suddenly matters, and NVIDIA can't ignore a competitor selling hardware at cost. Expect aggressive driver optimizations and maybe even a Linux-first marketing push.

4. Linux adoption is already exploding, more than past years. 2026 might really be the much mythologized year of desktop Linux. But if Intel ships a little Linux box into every other living room in America, OEMs and enterprise takes notice.

This isn't just a play for games. It's a real cultural reset.

However, Intel won't do it. It's not that it can't, as such, but it goes against two of their biggest anti-traits:

1. It requires restraint, which Intel doesn't have. They must NOT build an ecosystem or chase subscriptions.

2. It requires aggression, which Intel doesn't have. They MUST price it at cost and market it like a rebellion.

I really don't want or need any credit at all. But I love Intel. My inner child loves Intel. After working for them for a few years, even though my whole team was laid off last year, I want to see them succeed and return to cultural relevance. If this message reaches ONE person who has convincing power in the right meeting, hey, maybe it will happen.

Here's a niche request: last year I stumbled upon a personal website on HN, for a topic related to tech. The website also had a section on NYC, mostly Asian, restaurants that was great. I'm trying to find it again but to no avail. The design was fairly minimalist. Does it ring a bell to any one?

I have tried using the API to get all unique URLs on HN for last year, then crawling the websites to find pages matching relevant keywords but to no avail.

How to find the gaps and opportunities that can i build and earn some money to do some better?

Who remembers AWS's auction-based Spot instance market?

I'm researching the history of Spot instances and would love to hear from anyone who actually used AWS Spot during the auction era (before the 2017 pricing changes).

I've been reading research papers claiming AWS was manipulating prices with hidden reserve algorithms even though it marketed itself as an open auction.

Would love to hear if that matches real user experiences

I’m reaching out here because I’ve hit a wall that feels insurmountable and I am looking for genuine perspective from people who might have navigated systemic barriers.

I have a Master’s in Computer Science from a European university and a research background with papers totaling over 500 citations. In my spare time, I’m a builder and I’ve developed web apps, games, and various side projects. On paper, I should have a solid career path, but my reality is the opposite.

I am currently back in my home country in the East due to circumstances I couldn't control. Despite my credentials, my degree feels useless here. I work at a decent-sized western company (fully remote), but the internal politics are volatile and I fear for my job security. More painfully, I feel a deep sense of prejudice; in daily professional conversations, I can hear the tone shift when people realize where I am based. It feels like I am watching others reap what I have sown, while my own investments in skills and projects feel futile.

The core of my problem is twofold:

    Geographic and Legal: The jobs I am actually qualified for are almost exclusively in the West, but there is no current legal path for me to migrate or secure a living there.

    The Trust Gap: Despite the citations and my portfolio, I lack the "signal" that makes international recruiters or local employers trust my expertise. It feels like I'm "cursed" by my place of birth.

For those who have been stuck in "low-trust" geographies or faced extreme systemic barriers despite having high-level skills:

How do you bridge the trust gap with Western companies when relocation isn't an option? Are there specific niches (Remote-first R&D, specialized consulting, etc.) where academic citations and a builder mindset actually carry weight?

I am looking for any genuine suggestions on how to leverage what I have to secure a life that doesn't feel impossible.

No public announcement. Just quietly added to killed by google. Step 2 just fails with an error: https://developers.google.com/tenor/guides/quickstart

Hello Tenor API customer,

We're writing to inform you about the deprecation of the Tenor API offering on June 30, 2026. We are making this change to focus our resources on enhancing the main Tenor experience.

The Tenor API has been a valuable tool for many developers. We sincerely appreciate your partnership and the innovative ways you have integrated Tenor into your applications. Your contributions have helped shape the Tenor ecosystem.

What you need to know Key Dates:

Starting January 13, 2026, New API key sign-ups and new integrations will no longer be allowed On June 30, 2026, any API or Ads Distribution Agreements you have with Tenor will be terminated The Tenor API will continue to work for existing integrations until June 30, 2026 Advised Action:

Plan and make any necessary changes before June 30, 2026 Potential Impact:

After June 30, 2026, failure to transition out of the API will return an error message when attempting API requests Thanks for choosing Tenor API.

– The Google Cloud Team

The pain point I hear from plugin authors more than anything lately is "discoverability." It's not a new problem, but this new AI-driven landscape complicates it a bunch.

A while back I saw this LinkedIn post by Rand Fishkin. It revisits an old-school idea: brand mentions. Specifically, mentioning your brand right next to the same words everywhere on the web you can. And those words should be the pain point you solve. That tactic feels very relevant again because of how ChatGPT and other LLMs learn from public conversations.

People are trying to stop saying SEO and start saying things like AEO, LLMO…The term that makes the most sense to me is "Discovery Optimization"

Discovery doesn’t just happen in search results. It happens in forums, communities, product pages, and word of mouth. That’s how humans find things, and increasingly how AI does too.

So! I’m running a small experiment with my new free plugin: Synced Pattern Popups. I’m sharing the exact same clear value statement in as many credible, free places as I can. No rewriting per platform. Just clarity and consistency.

This is the line I’m using everywhere: “Synced Pattern Popups helps you create popups using the WordPress editor you already know, not yet another builder.”

I hear this constantly from plugin authors: “I shipped my free plugin, but I’ve been stuck under 10 active installs for months. What now?”

I’ve got plenty of theories, but nothing beats hands-on experience. So I’m testing this in public and seeing what actually moves the needle in this AI-shaped environment we all inherited.

So the question is: Is this how you do it? Do you all think this will gain any traction for a tiny free plugin? Looking forward to your feedback.

Yesterday, I had this thought of not using my insurance to full potential last year. So since this year is just starting out and I have a KP insurance I decided to understand what am I missing out on. I realized there's a lot. I had no clue that lot of preventive visits, tests are offered for free generally. I decided to then make up a custom GPT just for me to continue understanding it and have it give me a rough timeline of preventive test/visits that I should be doing this quarter. I ended up booking an appointment yesterday and have rough plan for this month which is going to cost me little to nothing. Have you thought about this?

Edit: All I initially had was a 100 page doc of my benefits that I never looked at by virtue of being lazy. So I dumped this in and gave it a task to optimize for cost and preventive tests.

Hi HN,

For those running LLMs in real production environments (especially agentic or tool-using systems): what’s actually worked for you to prevent confident but incorrect outputs?

Prompt engineering and basic filters help, but we’ve still seen cases where responses look fluent, structured, and reasonable — yet violate business rules, domain boundaries, or downstream assumptions.

I’m curious:

Do you rely on strict schemas or typed outputs?

Secondary validation models or rule engines?

Human-in-the-loop for certain classes of actions?

Hard constraints before execution (e.g., allow/deny lists)?

What approaches failed for you, and what held up under scale and real user behavior?

Interested in practical lessons and post-mortems rather than theory.

I keep hearing that prototyping is “solved” now — just use Cursor, Claude, Lovable, etc.

But when I talk to people inside real organizations (healthcare, regulated industries, even large non-tech companies), I keep seeing the opposite:

There’s no shortage of ideas. There’s a constant backlog of things people want to test — new workflows, internal tools, patient-facing flows, decision support UIs.

The bottleneck isn’t creativity. It’s: – internal IT teams focused on maintenance – engineers already overloaded – AI tools that still require time, context, and ownership – agencies/freelancers that are too slow or heavyweight for “just a prototype”

My hot take: AI didn’t eliminate the prototyping problem — it shifted it to the people who have the least time to deal with it.

Curious how this matches your experience: – Do you actually prototype continuously, or is it mostly one-off? – Have AI tools fully replaced the need for external help for you? – If you could get realistic prototypes in days (not months), how often would you use that?

Genuinely trying to understand whether I’m seeing a real pattern — or just a biased slice of the world.

Curious what broke first — pagination, tables, fonts, or maintainability.

Context: I’m asking because I’m building an open-source tool around server-side HTML → PDF and keep seeing teams try “lighter” libraries first, then fall back to headless Chrome in production.

Curious where that tipping point was for you.

In particular, I mean the research and intelligence vision these companies have been pushing for doing general and specialized physical labor.

I'm completely open to the possibility of having good telepresence in reliable hardware be a valuable addition to our economies. The hardware really is getting good these days, at useful payloads.

But what I'm particularly pessimistic about is seeing academic-type research try to fold clothes and put screws into holes with humanoid-like systems, raise 10s of $Mils, and form a company out of it. Ditto for existing humanoids.

One of the major humanoid companies (you can google/guess who) recently claimed that their humanoids will be doing surgeries in hospitals within 5 years? This is egregious, there is zero chance that becomes the platform of choice for doing surgery, over a special purpose-built bed + multi-arm platform specialized for said tasks. This is perhaps even worse that Rosie from the Jetsons vacuuming our homes before the Roomba is invented.

Showing another example, I have to get into specifics - https://www.youtube.com/watch?v=WlUFoZstcWg But I want to know, how could this possibly be better than setting up arms for this task, assuming it is to be done thousands of times? Is the change to the factory layout the main issue that makes this worth it? Even if it is... a single arm on a mobile platform certainly does this more economically.

Anyways, I see all this stuff, there's tons of money and hype and optimism, and I feel crazy being pessimistic, as I'm usually the techno optimist. So, do others feel similarly? Are there things I'm missing that could fuel some optimism perhaps?

Looking forward to the discussion.

Recently built something where simple domain-specific heuristics crushed a fancy ML approach I assumed would win. This has me thinking about how often we reach for complex tools when simpler ones would work better. Occam's razor moments.

Anyone have similar stories? Curious about cases where knowing your domain beat throwing compute at the problem.

I've often been in the market for new software engineer opportunities and have found some untraditional ways of finding new jobs. But with so many job search tools, AI and ATS matching I'm curious what job search strategies are working for you today? Also, what has been a waste of time?

Hello HN,

I've been working on Eleva.js, a minimalist frontend framework that just hit v1.0.0.

What it is: A 2.3KB (gzipped) vanilla JavaScript framework with signal-based reactivity and direct DOM patching.

Why I built it: I wanted something between writing raw DOM manipulation and using React/Vue. No JSX, no compiler, no virtual DOM — just native template literals and a simple reactivity primitive.

The mental model is intentionally small:

  const app = new Eleva("App");

  app.component("Counter", {
    setup({ signal }) {
      const count = signal(0);
      return { count, inc: () => count.value++ };
    },
    template: (ctx) => `
      <p>${ctx.count.value}</p>
      <button @click="inc">+</button>
    `
  });

  app.mount(document.body, "Counter");

- Signals for reactivity (similar to Solid/Preact signals) - Direct DOM diffing instead of virtual DOM - Render batching via queueMicrotask - No build step required — works with native ES modules - ~0.5KB/row memory overhead in Chrome benchmarks

Trade-offs:

- No SSR yet (client-side only) - Template strings aren't as composable as JSX - Smaller ecosystem than established frameworks

Links:

- Docs: https://elevajs.com - GitHub: https://github.com/TarekRaafat/eleva - npm: npm install eleva

Happy to answer questions about the implementation or design decisions.

I've been experimenting with using NotebookLM not just for summarization, but for "Red Teaming" my own automation workflows.

I fed it a video demo of an n8n research agent I built and used Custom Instructions to force the Audio Overview hosts into a specific persona: "Jaded, cynical tech reviewers."

My goal was to see if the model could pick up on specific node logic and architectural choices (like manual Server-Sent Events handling and JSON output forcing) from the video feed alone, rather than just summarizing the "gist" of the project.

The Prompt I used: "Act as two jaded, cynical tech reviewers... Focus ONLY on the single most impressive feature... Notice the 'Server-Sent Events' (SSE) configuration... Admit that the 4-Layer Architecture is legitimate engineering."

The Result: The model successfully identified the specific timeout handling logic I implemented in n8n, which a standard summarization usually misses. It seems like a viable way to get a "synthetic second opinion" on architectural flows if you prompt for hostility rather than agreement.

Video of the output (4 min): https://youtu.be/oof9JB3OFO4

Has anyone else had success forcing specific technical personas in NotebookLM?

I'm reaching out to the HN community because I've just lost something that can't be recreated: my entire GitHub history since I was 14 years old.

What happened?

My account was permanently banned without warning. After fighting through support tickets, the suspected culprit is a chargeback related to GitHub Copilot that occurred during a fraud dispute on my credit card.

When fraudulent charges were reversed, GitHub Copilot charges apparently went with them – and GitHub's automated system interpreted this as intentional fraud.

I started in infosec at 14 – hacking, building tools, contributing to open source on GitHub. As a teenager, I contributed work that was featured at DEFCON. I'm still early in my career, and I've been banking on something crucial: that real, tangible contributions to open-source projects would speak louder than any resume ever could. Those contributions were my resume. They were proof of work – thousands of commits, security tools, pull requests, issues, and collaborations that showed what I could actually build and how I work with others. All of that is now gone (unless you count the BigQuery archives...) Not suspended. Just... inaccessible.

The broader issue: For young developers and security researchers like me, GitHub contributions are our professional credibility. We don't have decades of corporate experience or impressive job titles. We have public code, meaningful contributions, security research, and a history of shipping. When that disappears overnight due to a banking mishap during a fraud dispute, it's devestating.

A fraudulent charge triggered a card cancellation In the dispute process, legitimate charges (including GitHub Copilot) were reversed GitHub's system flagged this as abuse and permanently banned the account No warning. No appeal process. No way to distinguish fraud victims from bad actors.

If anyone has connections at GitHub who can review this with human judgment, please reach out. I am desperate for a 2nd chance. If you've successfully navigated a similar situation, let me know.

I understand platforms need to combat fraud, but there has to be room for nuance. A mistaken reversal during a legitimate fraud dispute shouldn't permanently erase years of work. I'm ready to immediately settle any disputed charges and provide whatever documentation is needed – but I can't even get to a human who can evaluate the situation.

How You Can Help

If anyone has connections at GitHub who can review this with human judgment, please reach out. I am desperate for a 2nd chance. If you've successfully navigated a similar situation, let me know.

I understand platforms need to combat fraud, but there has to be room for nuance. A mistaken reversal during a legitimate fraud dispute shouldn't permanently erase years of work. I'm ready to immediately settle any disputed charges and provide whatever documentation is needed – but I can't even get to a human who can evaluate the situation.

---

Contact Info

Email: nico@omg.lol

GitHub (banned acct): nicoandmee

StackOverflow: https://stackoverflow.com/users/6934588/nico-mee

Keybase: https://keybase.io/nicomee

Personal Website: https://nicomee.com/